# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from utils import EVALUATORS
from .base import BaseRetrievalEvaluator
from collections import defaultdict
from sklearn.metrics import ndcg_score


@EVALUATORS.register_module("mmeb_metrics")
class MMEBEvaluator(BaseRetrievalEvaluator):
    def __init__(self, retrieval_result, **kwargs):
        super().__init__(retrieval_result, **kwargs)

    def score(self):
        correct = 0
        total = 0
        single_query_score_list = []
        for _, _, eval_result_dict in self.retrieval_result:
            for _, eval_result in eval_result_dict.items():
                pred, scores = eval_result
                total += 1
                if pred == 0:
                    correct += 1
        return {
            "accuracy": round((correct / float(total)) * 100, 4),
        }
